Systems and methods for automated wetstock management

ABSTRACT

An automated wetstock management system can include a plurality of sensors disposed in a fuel storage facility, the plurality of sensors configured to sense fuel data characterizing one or more aspects of the fuel storage facility, and a wetstock management server communicatively coupled to the plurality of sensors. The wetstock management server can process the fuel data to detect whether the fuel data satisfies an exception indicative of an operational issue of the fuel storage facility based on one or more predefined rules or models stored in the wetstock management server. In some embodiments, the wetstock management server can generate a workflow for assisting a user of the fuel storage facility to resolve the operational issue. In some embodiments, the wetstock management server can assign a risk category to the exception and electronically transmit an alert characterizing the operational issue to the user.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Pat. App. No. 16/506,614,filed Jul. 9, 2019 and entitled “SYSTEMS AND METHODS FOR AUTOMATEDWETSTOCK MANAGEMENT,” the entire contents of which are hereby expresslyincorporated by reference herein.

FIELD

Systems and methods are provided for automated management of wetstock.

BACKGROUND

Wetstock management is an essential function in day-to-day operations ofa fuel storage facility. Typically, wetstock management can involve themonitoring of fuel stock at a fuel storage facility using a variety ofmeasurement devices, such as automatic tank gauges (ATGs), fuel leakdetection sensors, magnetostrictive probes, and so forth, evaluatingmeasurements to detect abnormal, and often unsafe, events affecting thefuel stock (e.g., fuel losses, fuel excesses, tank defects, operationalissues, etc.), and performing corrective actions as necessary.

Traditionally, wetstock measurements can be evaluated manually by astorage facility operator. The operator can be responsible formonitoring the measurements in order to identify anomalies and respondappropriately. However, the practice of relying upon humans to manuallymonitor large volumes of sensor data can be error prone, potentiallyresulting in the failure to detect and resolve problems at an earlystage. Such failure, in the context of wetstock management, couldproduce catastrophic consequences such as environmental contamination,loss of revenue, damaged reputation, and public health risks.

SUMMARY

Methods and devices are provided for automated wetstock management. Inone exemplary embodiment, one or more of a plurality of sensors disposedin a fuel storage facility can sense fuel data characterizing one ormore aspects of the fuel storage facility. A wetstock management server,which is communicatively coupled to the plurality of sensors, canprocess the fuel data to detect whether the fuel data satisfies anexception indicative of an operational issue of the fuel storagefacility based on one or more predefined rules or models stored in thewetstock management server.

In certain exemplary embodiments, the wetstock management server canidentify an operational issue of the fuel storage facility based on theexception when said exception is detected. The wetstock managementserver can then automatically generate a workflow including a series ofsteps for assisting one or more users of the fuel storage facility toresolve the identified operational issue. Furthermore, a devicecommunicatively coupled to the wetstock management server can display avisual characterization of the workflow using a display unit of thedevice

In certain exemplary embodiments, the wetstock management server canassign a risk category among a plurality of predefined risk categoriesto the exception based on one or more exception criteria associated witheach of the plurality of predefined risk categories when said exceptionis detected. Based on the identified risk category, the wetstockmanagement server can automatically select one or more electroniccommunication channels and electronically transmit an alertcharacterizing the operational issue to the one or more users via theone or more selected electronic communication channels.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIG. 1 is a flowchart illustrating an exemplary overview of an automatedwetstock management system;

FIG. 2 is an exemplary user interface implemented by the automatedwetstock management system of FIG. 1 ; and

FIG. 3 is a flowchart illustrating an exemplary, simplified procedureimplemented by the automated wetstock management system of FIG. 1 .

It should be understood that the above-referenced drawings are notnecessarily to scale, presenting a somewhat simplified representation ofvarious preferred features illustrative of the basic principles of thedisclosure. The specific design features of the present disclosure,including, for example, specific dimensions, orientations, locations,and shapes, will be determined in part by the particular intendedapplication and use environment.

DETAILED DESCRIPTION

Wetstock management can involve the usage of fuel data sensors tomonitor the fuel stock at a fuel storage facility, evaluatingmeasurement data to detect anomalies affecting the fuel stock, andperforming corrective actions as necessary. The sensors can measure fueldata characterizing myriad possible aspects of the fuel storagefacility. For example, fuel losses due to leaks, theft, deliveryshortages, or the like can be detected and damage to storage equipmentcan be identified. By automating these processes in a “smart” mannerapplying, for instance, artificial intelligence and/or machine learningtechniques, as described below, wetstock management can be performedmore efficiently, economically, and safely.

Embodiments of methods and systems for automated wetstock management arediscussed herein below.

FIG. 1 illustrates one embodiment of an exemplary automated wetstockmanagement system. The automated wetstock management system (“wetstocksystem”) 100 can provide an automated, alert-driven wetstock managementservice using automated processing, artificial intelligence, and/ormachine learning technologies to dynamically create workflows for thepurpose of resolving issues arising in a given fuel storage facility. Insome embodiments, operation of the wetstock system 100 can be carriedout by a remote, cloud-based wetstock management server (not shown)configured to perform one or more operations described below. Thewetstock system 100 can collect electronic fuel data characterizing oneor more aspects of the fuel storage facility including, but not limitedto, fuel stored in the facility, storage equipment (e.g., tanks),monitoring equipment, etc., from multiple sources such as, for example,automatic tank gauges (ATGs), point of sale devices, forecourtcontrollers, back office systems, fuel dispensers, and the like, as wellas manually submitted fuel data (e.g., through a wetstock managementcomputer application, website, etc.). The wetstock system 100 canprocess the collected data automatically through various algorithms,machine learning, and/or artificial intelligence to create alerts and/orexceptions.

Additionally, the wetstock system 100 can apply risk categorization to adynamically created diagnostic workflow. The wetstock system 100 canevaluate incoming data when an exception is raised, validate theexception to filter out any invalid exceptions, categorize the exceptionbased on risk, identify the most likely fault, as well as theprobability of said fault, and notify a user (e.g., fuel storagefacility operator or manager, fuel merchant, etc.) in a bespoke manner.In addition to exception-specific faults, the wetstock system 100 canutilize the individual exceptions to generate a consolidatedrisk-versus-probability model and solution. Finally, resolved issues canbe tracked and logged by the wetstock system 100. The data can used astraining data in a machine learning context to enable the wetstocksystem 100 to learn from previous diagnoses and more effectivelydiagnose similar situations in the future.

According to some embodiments, as shown in FIG. 1 , the wetstock system100 can be configured to embody a supporting component 110 and ananalytics component 120, each of which composed of multiple individualelements. However, the wetstock system 100 is not limited solely to suchconfiguration. The supporting component 110 of the wetstock system 100can include the components necessary to enable an analytics model,implemented by the analytics component 120 to function and be serviced.The analytics component 120 of the wetstock system 100 can include thecomponents according to which collected fuel data is processed andanalyzed. It is understood that the supporting and analytics components110 and 120 are not limited solely to the configurations shown in FIG. 1and described below, but can be re-configured and/or re-arranged in anysuitable manner, as would be understood by a person of ordinary skill inthe art, consistent with the scope of the present claims defined herein.

Operationally, the wetstock system 100 can execute individual units ofthe supporting and analytics components 110 and 120 in a particularorder, such as the order depicted in FIG. 1 . However, the ordering ofunits shown in FIG. 1 is provided merely for demonstration purposes, andoperation of the wetstock system 100 is not limited solely thereto.Thus, units of the supporting and analytics components 110 and 120,respectively, can be executed in any suitable order, as would beunderstood by a person of ordinary skill in the art, consistent with thescope of the present claims defined herein.

Referring now to FIG. 1 , the supporting component 110 can initializethe wetstock system 100 by executing units that support or enableoperation of the analytics component 120 whereby the collected fuel datais automatically processed and analyzed. Firstly, for example, anonboarding unit 111 can be executed whereby features required toinitialize organizations, sites, wetstock details, and the like arecarried out. Similarly, a user and system management unit 112 can beperformed whereby features required to initialize one or more users(e.g., fuel storage facility operator or manager, fuel merchant, etc.)of the wetstock system 100 are carried out. For example, one or moreregistered users of the wetstock system 100 can be loaded, preferencesof the one or more users can be imported, user permissions can be set,and so forth. In addition, features required to initialize the wetstocksystem 100 itself can be executed. For example, security settings, sitegroups, and the like associated with the wetstock system 100 can beinitialized. The user and system management unit 112 can initialize userand system settings using operation data stored in a local or remotememory (not shown), depending on the configuration, characterizing oneor more aspects of previous operations of the wetstock system 100. Incases where said operation data does not exist, the user and systemmanagement unit 112 can initialize user and system settings according toa default configuration.

Once the supporting component 110 has initialized the wetstock system100 by executing units that support or enable operation of the analyticscomponent 120, units of the analytics component 120 can be executed.Firstly, for example, a data processing unit 121, which encompasses boththe import and export of data, can be performed. In detail, the dataprocessing unit 121 can begin by collecting fuel data characterizing oneor more aspects of a fuel storage facility from a wide variety ofdevices such as sensors or other measurement tools. These devices caninclude, for example, ATGs, fuel leak detection sensors,magnetostrictive probes, point of sale devices, forecourt controllers,back office systems, fuel dispensers, and so on. Also, users of thewetstock system 100 can manually submit data to be processed. Allinputted data can be combined and exported for automated evaluationusing predefined algorithms (122) of the wetstock system 100.

An algorithms unit 122 can then be executed whereby the fuel datacollected in the data processing unit 121 is inputted to one or morepredefined models and/or rules of the wetstock system 100. Algorithms ofthe wetstock system 100 can include any models and/or rules forprocessing the collected fuel data to generate one or more exceptions(123) when said one or more exceptions exist. The algorithms can be usedto evaluate the collected input data for myriad purposes such asanalyzing fuel loss, flow rates, delivery yields, etc., in order toalert the user of any issues occurring in the fuel storage facility.Such algorithms can encompass, but are not limited to, anomaly detection(e.g., detecting the presence of a value outside of a calculated orpre-configured normal range for a value during a given time slice),trend analysis (e.g., detecting the tendency of a value to move toward arange considered to be unacceptable), cross-value correlation (e.g.,detecting the tendency of a value to change based on values of anothervariable or external events), and so on. To these ends, the input datacan be analyzed in various ways such as calculating a maximum or minimumvalue over a given time slice, calculating an average, mean, or medianvalue over a given time slice, calculating a standard deviation over agiven time slice, and so on. For example, an average value in a giventime slice (e.g., week, month, quarter, year, etc.) can be compared witha corresponding average value associated with a past time slice todetect anomalies. In some embodiments, multiple algorithms can becombined to create new algorithms. Output data generated by execution ofthese algorithms can be used to identify exceptions, escalate risk,and/or apply to further algorithms.

Next, an exceptions and services unit 123 can be executed wherebyexceptions generated through the processing of collected fuel data viathe algorithms described above can be delivered to the user. For thepurpose of the present disclosure, an exception can refer to any dataoutputted via the algorithms unit 122 having a value which is outside ofa predefined normal, or safe, range or threshold. For example, a fuelleak can cause a sudden decrease in fuel tank level. If an ATG detectsthat said level is less than a predefined minimum tank level threshold,an exception indicative of a fuel leak can be present. A wide range ofservices can be provided to the user based on the generated exceptionsincluding, for example, predictive maintenance of impending faultyequipment, regulatory report generation and delivery to the appropriatestandards bodies, authority notification when product theft is detected,predictive delivery of product based on trends, vendor notification ofincorrect delivery of product (e.g., insufficient delivery, incorrectproduct, etc.), automated shutdown of fuel pumps due to detected issues(e.g., leaks, mechanical pump issues, etc.), and so on.

Next, a risk escalation unit 124 can be executed whereby a risk categorycan be assigned to an exception generated through the algorithms unit122 and exceptions and services unit 123 based on a variety of factors.The risk assignment can be used to determine whether or not to escalatethe exception, as well as the extent to which the exception isescalated. Moreover, risk categorization can allow users to assign rulesto a particular risk category that is specific to their needs. In somecases, as anomalies in the input data are detected and exceptions aregenerated in the manner described above, a machine learning-based systemcan examine the actions taken to address an anomalous situation, such asa fuel leak, as it occurs in real-time. Thus, when the exceptionre-occurs, the response time can be compared against both configuredservice level agreements and past resolutions to determine whether thecorrect resources are being applied and the appropriate attention isbeing given to the fuel leak. Further, as new anomalies in the fuel dataare detected, and exceptions are generated which can exacerbate thesituation, machine learning techniques can use the past resolutions astraining data to change the resources assigned or invoke automatedreactions to a new higher or lower risk. Examples of these reactionscould be shutting down devices, notifying the authorities, notifyingmore experienced personnel, and so on.

Next, a workflow unit 125 can be executed whereby a workflow including aseries of steps for assisting the user to resolve an identifiedoperational issue can be generated in real-time based on an identifiedexception and the risk category assigned thereto. The workflow canprovide end-to-end support for the user to resolve an operational issuein the most appropriate and efficient manner, taking into accounton-site equipment, depending on the threat and seriousness of the issue.For example, when the operational issue is a fuel leak, the workflow caninclude steps intended to correct or prevent exacerbation of the fuelleak. The workflow can be provided to the user in a manner determinedaccording to the generated exception and the level of risk assignedthereto. In some embodiments, a device (e.g., a computing device such asa computer, mobile device, tablet device, etc.) coupled to a wetstockmanagement server (not shown) responsible for performing elements of theanalytics component 120 can display a visual characterization of theworkflow, via a display unit of the device, enabling the user to readand follow the displayed workflow steps.

Next, a notifications unit 126 can be executed whereby a notification oralert, each of which is used interchangeably herein, characterizing theoperational issue can be generated and sent to the user through avariety of possible communication channels or mechanisms. Thenotifications can be generated to allow for specific messages andchannels of communications to be used depending on the type of alert.Multiple different users can be notified at a time which can vary basedon the time of day. Also, the notification can be created andtransmitted in a manner determined based on the assigned risk category,such that users are alerted only to on-site equipment issue when certainrules and/or risks are breached.

Upon resolution of the operational issue, e.g., a detected fuel leak hasbeen eliminated, a data reassessment unit 127, a resolve and learningunit 128, and a tools unit 113 can be executed, thereby completing theanalytics component 120 and the supporting component 110 for theparticular exception. The assigned risk category can be de-escalated inresponse to issue resolution, but the fuel data can still be collectedand monitored to ensure the exception no longer occurs. Moreover, thewetstock system 100 can maintain records for continual improvementthereof, such as validation of the workflow and training of models. Inthis regard, machine learning techniques can be applied to train rules,thresholds, and/or settings, using available information (e.g.,collected fuel data, generated exception, workflow, notifications, etc.)as input. As a result, the workflows and notifications provided throughthe wetstock system 100 in response to exceptions can improve throughoutthe operational lifespan of the system.

As an illustrative example, it is assumed that a fuel storage facilityis equipped with a tank overfill alarm that activates when an ATGcoupled to a fuel tank detects a particular stock level volume. A fueldata collection device (not shown), such as an Internet of Things (IoT)device, located on-site can collect the ATG data and transmit thecollected data to a remotely located wetstock management server (notshown) configured to perform operations of the wetstock system 100.Particularly, the wetstock management server can execute theaforementioned units of the analytics components 120 including the dataprocessing unit 121 to collect the ATG data from the fuel datacollection device in conjunction with fuel data measured by otheron-site devices and/or manually inputted data, the algorithms unit 122to process the collected data according to one or more predefined rulesand/or models, the exceptions and services unit 123 to determine whetheran exception, e.g., a fuel tank level outside of a safe range, exists,the risk escalation unit 124 to assign a detected exception a riskcategory, and the workflow unit 125 to generate a workflow providingend-to-end support for the user to resolve the issue causing theexception.

The wetstock management server can further execute the notificationsunit 126 to determine a notifying action dependent upon user-specificand tank-specific settings. In some cases, multiple risk categories eachof which corresponding to one or more predefined exception criteria canbe created. Each risk category can also correspond to one or morechannels of electronic communication through which a notification is tobe delivered, such as an automated phone call, short message service(SMS) message (text message), e-mail, push notification to wetstockmanagement application, and so on. As the risk category increases inurgency, more communication channels can be selected for transmission ofthe notification. A risk category among the plurality of possible riskcategories can be assigned to the exception based upon the exceptioncriteria(s) associated with each risk category. For illustration, anexample set of risk categories and corresponding criteria andcommunication channel is provided below in Table 1.

TABLE 1 Risk Category Type Exception Criteria Communication Channel RiskCategory 1 Nominal capacity has been equaled or exceeded Phone call;SMS; E-mail; Push notification Risk Category 2 Safe working capacity(SWC) has been breached by more than 100 liters SMS; Push notificationRisk Category 3 SWC has been breached by less than 100 liters Pushnotification Risk Category 4 SWC has not been breached No alert

Based on the risk category of the generated exception, a notificationdescribing the operational issue can be generated and electronicallytransmitted via the corresponding electronic communication channel(s).The notification can include any available data characterizing thenature of the issue. As the risk of the operational issue, e.g., theseverity of the fuel leak, increases, so too does the number ofcommunication channels through which the notification is transmitted.The day and time of the detected operational issue can determine theuser or users that are notified.

The notified user(s) can then log on to an application of the wetstocksystem 100 and review the workflow generated by the wetstock managementserver for the exception that will guide them through resolving theissue (e.g., fuel leak). The workflow can include recommendations suchas checking particular locations, e.g., interceptors, forecourt sensors,etc., for signs of fuel spills, contacting relevant authorities orresponse teams, and so on. After the issue has been resolved, thewetstock system 100 can maintain records for learning purposes so thatin the event of a future stock reading at the same height, the risk ofsuch issue is already known and can be more efficiently addressed.

The wetstock system 100 can implement a wetstock management computerapplication with a user interface through which the user can interactwith the wetstock system 100 by viewing workflows, receivingnotifications (e.g., push notifications), and the like. In this regard,FIG. 2 is an exemplary user interface implemented by the wetstock system100. The user interface 200 can include a variety of interactiveelements intended to inform the user of information characterizing oneor more aspects of the fuel storage facility provided by the wetstocksystem 100. For example, the user interface 200 can include a test siteselection section 210 in which the user can select a test site of thefuel storage facility (e.g., Test Site 1), as well as a particular tank(e.g., Tank 1) of the selected site. The test site selection section 210can also include selectable elements (e.g., buttons, drop-down menus,test input bars, etc.) enabling the user to quickly select a currentsite, receive confidence predictions (described below), and/or reset allinput data collected by the wetstock system 100.

Additionally, the user interface 200 can include a fuel data section 220displaying information based on collected fuel data characterizing oneor more aspects of the fuel storage facility. For example, the fuel datasection 220 can include status indicators of sensors, alarms, and soforth within the fuel storage facility. Moreover, the fuel data section220 can include visual indicators of both active and inactive exceptionsas determined automatically by the wetstock system 100 based upon thecollected fuel data. As shown in FIG. 2 , the fuel data objects 221 canindicate inactive exceptions, while the fuel data objects 222 canindicate active exceptions. As such, the wetstock system 100 is notlimited to recognizing only a single exception at a time, but canrecognize multiple exceptions under certain circumstances.

In some embodiments, the wetstock system 100 can combine the multipleexceptions as indicated by fuel data objects 221 and 222 in order topredict the most likely cause of the exceptions. In this regard, theuser interface 200 can include a confidence prediction section 230 inwhich one or more possible faults are provided in order of probabilitycalculated using wetstock system 100 analytics described above. As shownin FIG. 2 , for example, a line issue can be predicted as the mostlikely fault or cause of the current exceptions. Based upon thepredicted most likely fault, the wetstock system 100 can generate aworkflow in the manner described above, which can be displayed for theuser through the user interface 200. In some embodiments, the user canselect a particular predicted cause of the exceptions, and the workflowcan be generated based upon the selected cause.

FIG. 3 is a flowchart illustrating an exemplary, simplified procedureimplemented by the wetstock system 100. The procedure 300 can start atstep 305, and continue to step 310, where, as described in greaterdetail below, the wetstock system 100 can perform automated wetstockmanagement to enable resolution of exceptions identified duringoperation of a fuel storage facility.

At step 305, one or more sensors of a plurality of sensors (e.g., ATGs,fuel leak detection sensors, magnetostrictive probes, point of saledevices, forecourt controllers, back office systems, fuel dispensers,etc.) disposed in the fuel storage facility can sense fuel data of thefuel storage facility. The fuel data can include any type of measurementdata characterizing one or more aspects of the fuel storage facilityincluding, for example, fuel tank levels, water content, leak detection,flow readings, equipment status, and so on.

At step 310, a wetstock management server communicatively coupled to theplurality of sensors can collect the acquired fuel data, via dataprocessing unit 121, and process the fuel data to detect whether thefuel data satisfies an exception indicative of an operational issue ofthe fuel storage facility based on one or more predefined rules ormodels stored in the wetstock management server, via algorithms unit 122and exceptions and services unit 123. The wetstock management server canbe a remote, i.e., cloud-based, server located outside of the fuelstorage facility. In some embodiments, the measured fuel data can becollected by a fuel data collection device (not shown), such as an IoTdevice, located on-site, and the fuel data collection device cantransmit the collected data to the wetstock management server forprocessing.

Upon detecting that the collected fuel data satisfies an exception, theprocedure 300 can proceed toward one or more outputs includinggenerating and displaying a workflow (steps 315 through 325) andidentifying a risk category and transmitting an alert via selectcommunication channels (steps 330 through 340). In some embodiments,only one of the outputs can be carried out. In other embodiments, bothoutputs, or any combination thereof, can be carried out.

At step 315, the operational issue of the fuel storage facility can beidentified based on the exception. For instance, if the exceptionderives from a sudden decrease in a fuel tank level, the operationalissue can be identified as a fuel loss or leak.

At step 320, a workflow can be generated, via workflow unit 125, forassisting a user of the fuel storage facility to resolve the operationalissue identified in step 315. The workflow can include a series of stepsproviding end-to-end support for the user to resolve the operationalissue in the most appropriate and efficient manner. The workflow can begenerated dynamically, that is, in real-time, taking into accounton-site equipment and the seriousness of the issue.

At step 325, a device communicatively coupled to the wetstock managementserver can display a visual characterization of the workflow, such as alisting of the workflow steps. The device, e.g., a computer, a mobiledevice, a tablet, or the like, can include a display unit configured todisplay the visual characterization of the workflow. In someembodiments, the user can interact with the device by, for example,indicating through the device that a workflow step has been completed,that additional assistance is necessary, or the like.

Meanwhile, at step 330, a risk category among a plurality of predefinedrisk categories can be assigned to the exception, via risk escalationunit 124. Identifying the risk category can be carried out based on oneor more exception criteria associated with each of the plurality ofpredefined risk categories. For example, as shown in Table 1, criteriarelating to an amount by which fuel exceeds a predefined maximum limitcan correspond to each risk category. The exception detected in step 310can be compared with the exception criteria to assign the appropriaterisk category to the exception.

At step 335, one or more electronic communication channels among aplurality of predefined electronic communication channels can beselected for transmission of an alert characterizing the operationalissue to the user. Referring again to Table 1, each of the predefinedrisk categories can correspond to a particular set of electroniccommunication channels. Thus, the one or more electronic communicationchannels can be selected based on the assigned risk category.

At step 340, an alert or notification characterizing the operationalissue can be electronically transmitted to the user, via notificationsunit 126, using the one or more electronic communication channelsselected in step 335. The number of users receiving the alert can dependupon the degree of urgency associated with the assigned risk category aswell as the date and time at which the exception is detected. Also, thealert can be created and transmitted such that users are alerted only toon-site equipment issues when certain rules and/or risks are breached.

The procedure 300 can continue throughout operation of the fuel storagefacility. The techniques by which the steps of procedure 300 may beperformed, as well as ancillary procedures and exception criteria, aredescribed in detail above.

It should be noted that the steps shown in FIG. 3 are merely examplesfor illustration, and certain other steps may be included or excluded asdesired. Further, while a particular order of the steps is shown, thisordering is merely illustrative, and any suitable arrangement of thesteps may be utilized without departing from the scope of theembodiments herein. Even further, the illustrated steps may be modifiedin any suitable manner in accordance with the scope of the presentclaims.

Accordingly, the automated wetstock management system as discussedherein can combine all known alerts and data points, site equipment, andinfrastructure details into a model to provide a user with the mostlikely on-site fault based on both risk, likelihood, real-lifeprobability, and the equipment on-site. By applying artificialintelligence and machine learning techniques to wetstock managementprocedures, wetstock management can be performed more efficiently,thereby saving costs and improving safety.

It should be understood that terminology used herein is for the purposeof describing particular embodiments only and is not intended to belimiting of the disclosure. As used herein, the singular forms “a,”“an,” and “the” are intended to include the plural forms as well, unlessthe context clearly indicates otherwise. It will be further understoodthat the terms “comprises,” “includes,” or variations thereof, when usedin this specification, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items. The term“coupled” denotes a physical relationship between two components wherebythe components are either directly connected to one another orindirectly connected via one or more intermediary components.

Approximating language, as used herein throughout the specification andclaims, may be applied to modify any quantitative representation thatcould permissibly vary without resulting in a change in the basicfunction to which it is related. Accordingly, a value modified by a termor terms, such as “about,” “approximately,” and “substantially,” are notto be limited to the precise value specified. In at least someinstances, the approximating language may correspond to the precision ofan instrument for measuring the value. Here and throughout thespecification and claims, range limitations may be combined and/orinterchanged, such ranges are identified and include all the sub-rangescontained therein unless context or language indicates otherwise.

Additionally, it is understood that one or more of the above methods, oraspects thereof, may be executed by at least one control unit. The term“control unit” may refer to a hardware device that includes a memory anda processor. The memory is configured to store program instructions, andthe processor is specifically programmed to execute the programinstructions to perform one or more processes which are described above.The control unit may control operation of units, modules, parts,devices, or the like, as described herein. Moreover, it is understoodthat the above methods may be executed by an apparatus, such as awetstock management server, comprising the control unit in conjunctionwith one or more other components, as would be appreciated by a personof ordinary skill in the art.

The foregoing description has been directed to embodiments of thepresent disclosure. It will be apparent, however, that other variationsand modifications may be made to the described embodiments, with theattainment of some or all of their advantages. Accordingly, thisdescription is to be taken only by way of example and not to otherwiselimit the scope of the embodiments herein. Therefore, it is the objectof the appended claims to cover all such variations and modifications ascome within the true spirit and scope of the embodiments herein.

1. A method for automated wetstock management comprising: sensing, usingone or more of a plurality of sensors disposed in a fuel storagefacility, fuel data characterizing one or more aspects of the fuelstorage facility; processing, by a wetstock management servercommunicatively coupled to the plurality of sensors, the fuel data todetect whether the fuel data satisfies an exception indicative of anoperational issue of the fuel storage facility based on one or morepredefined rules or models stored in the wetstock management server; andin response to detecting that the fuel data satisfies the exception:identifying, by the wetstock management server, the operational issue ofthe fuel storage facility based on the exception, generating, by thewetstock management server, a workflow including a series of steps forassisting one or more users of the fuel storage facility to resolve theidentified operational issue, and causing, by the wetstock managementserver, a device communicatively coupled to the wetstock managementserver to display a visual characterization of the workflow using adisplay unit of the device.
 2. The method of claim 1, wherein thedetecting of whether the fuel data satisfies the exception comprises:obtaining a current fuel level based on the fuel data; and determiningwhether the current fuel level exceeds a predefined capacity of a fueltank.
 3. The method of claim 1, wherein the visual characterizationincludes analytical information of the fuel data and one or morerecommended actions for resolving the operational issue.
 4. The methodof claim 1, further comprising: storing, by the wetstock managementserver, the fuel data in a storage unit; and training, by the wetstockmanagement server, a machine learning algorithm using the fuel data anddata characterizing the operational issue of the fuel storage facility,the machine learning algorithm operable to accept a given dataset asinput and identify the operational issue of the fuel storage facility asoutput.
 5. The method of claim 1, wherein the fuel data includesmeasurements obtained by two or more of the plurality of sensors.
 6. Themethod of claim 1, further comprising: determining, by the wetstockmanagement server, whether the fuel data satisfies any of a plurality ofpredefined exceptions, respectively; and in response to determining thatone or more of the plurality of predefined exceptions are satisfied,identifying, by the wetstock management server, the operational issue ofthe fuel storage facility based on the one or more predefined exceptionsthat are satisfied.
 7. The method of claim 6, further comprising:determining, by the wetstock management server, a plurality of possibleoperational issues of the fuel storage facility based on the one or morepredefined exceptions that are satisfied; generating, by the wetstockmanagement server, a probability associated with each of the pluralityof possible operational issues; and identifying, by the wetstockmanagement server, the operational issue of the fuel storage facilitybased on the possible operational issue among the plurality of possibleoperational issues associated with the highest generated probability. 8.The method of claim 6, further comprising: causing, by the wetstockmanagement server, the device to display a visual characterization ofthe one or more predefined exceptions that are satisfied via the displayunit.
 9. The method of claim 8, wherein the visual characterizationincludes an indication of whether the one or more predefined exceptionsthat are satisfied are active or inactive.
 10. A system for automatedwetstock management comprising: a plurality of sensors disposed in afuel storage facility, the plurality of sensors configured to sense fueldata characterizing one or more aspects of the fuel storage facility; awetstock management server communicatively coupled to the plurality ofsensors, the wetstock management server configured to: process the fueldata to detect whether the fuel data satisfies an exception indicativeof an operational issue of the fuel storage facility based on one ormore predefined rules or models stored in the wetstock managementserver, identify the operational issue of the fuel storage facilitybased on the exception, and generate a workflow including a series ofsteps for assisting one or more users of the fuel storage facility toresolve the identified operational issue; and a device communicativelycoupled to the wetstock management server, the device including adisplay unit, wherein the wetstock management server is configured tocause the device to display a visual characterization of the workflowvia the display unit.
 11. A method for automated wetstock managementcomprising: sensing, using one or more of a plurality of sensorsdisposed in a fuel storage facility, fuel data characterizing one ormore aspects of the fuel storage facility; processing, by a wetstockmanagement server communicatively coupled to the plurality of sensors,the fuel data to detect whether the fuel data satisfies an exceptionindicative of an operational issue of the fuel storage facility based onone or more predefined rules or models stored in the wetstock managementserver; and in response to detecting that the fuel data satisfies theexception: assigning, by the wetstock management server, a risk categoryamong a plurality of predefined risk categories to the exception basedon one or more exception criteria associated with each of the pluralityof predefined risk categories, selecting, by the wetstock managementserver, one or more electronic communication channels among a pluralityof predefined electronic communication channels based on the assignedrisk category, and electronically transmitting, by the wetstockmanagement server, an alert characterizing the operational issue to oneor more users via the one or more selected electronic communicationchannels.
 12. The method of claim 11, wherein the assigning of the riskcategory to the exception comprises: obtaining, by the wetstockmanagement server, a current fuel level based on the fuel data;calculating, by the wetstock management server, a difference between thecurrent fuel level and a predefined capacity of a fuel tank; andassigning, by the wetstock management server, the risk category to theexception based on the calculated difference.
 13. The method of claim11, wherein the plurality of predefined electronic communicationchannels include two or more of e-mail, short message service (SMS), apush notification through a device application, an automated telephoniccall.
 14. The method of claim 11, wherein the selecting of the one ormore electronic communication channels comprises: selecting, by thewetstock management server, a first number of the plurality ofpredefined electronic communication channels when the assigned riskcategory is a first category; and selecting, by the wetstock managementserver, a second number of the plurality of predefined electroniccommunication channels, the second number being greater than the firstnumber, when the assigned risk category is a second category associatedwith a risk higher than a risk associated with the first category. 15.The method of claim 11, further comprising: selecting, by the wetstockmanagement server, the one or more users to receive the alertcharacterizing the operational issue based on a current date and time.16. The method of claim 11, further comprising: generating, by thewetstock management server, a workflow including a series of steps forassisting the one or more users to resolve an operational issueassociated with the exception; and electronically providing, by thewetstock management server, the workflow to the one or more users. 17.The method of claim 16, wherein the electronic providing of the workflowcomprises: causing, by the wetstock management server, a devicecommunicatively coupled to the wetstock management server to display avisual characterization of the workflow using a display unit of thedevice.
 18. The method of claim 11, further comprising: storing, by thewetstock management server, the fuel data in a storage unit; andtraining, by the wetstock management server, a machine learningalgorithm using the fuel data, the machine learning algorithm operableto accept a given dataset as input and assign a risk category among theplurality of predefined risk categories to the exception as output. 19.The method of claim 11, further comprising: determining, by the wetstockmanagement server, whether the fuel data no longer satisfies theexception; and electronically transmitting, by the wetstock managementserver, a message indicating that the exception is no longer satisfiedto the one or more users when the exception is no longer satisfied. 20.A system for automated wetstock management comprising: a plurality ofsensors disposed in a fuel storage facility, the plurality of sensorsconfigured to sense fuel data characterizing one or more aspects of thefuel storage facility; a wetstock management server communicativelycoupled to the plurality of sensors, the wetstock management serverconfigured to: process the fuel data to detect whether the fuel datasatisfies an exception indicative of an operational issue of the fuelstorage facility based on one or more predefined rules or models storedin the wetstock management server, assign a risk category among aplurality of predefined risk categories to the exception based on one ormore exception criteria associated with each of the plurality ofpredefined risk categories, select one or more electronic communicationchannels among a plurality of predefined electronic communicationchannels based on the assigned risk category, and electronicallytransmit an alert characterizing the operational issue to one or moreusers via the one or more selected electronic communication channels.